Method and apparatus to visualize the coronary arteries using ultrasound

ABSTRACT

A non-invasive screening technique for visualizing coronary arteries which overcomes the problems of visualizing the curved arteries by projecting the three dimensional volume of the arteries onto a two dimensional screen. Blood-filled areas such as the coronary arteries and veins, are highlighted to contrast with other nearby tissues using non-linear classification and segmentation techniques. Data is gathered as a sequence of 2D slices stored as a 3D volume. Software interpolates voxels intermediate to the slices. Wiener filtering or LMS spatial filtering can be implemented on each 2D scan to improve lateral resolution and reduce noise prior to the use of the scan data with the classification and segmentation algorithms. A traditional handheld ultrasound probe is employed to enable the technician to locate the area of interest, but a gyroscopic stabilizer is added to minimize unwanted variation on two axes of rotation.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/532,013 filed Sep. 14, 2006 now U.S. Pat. No. 8,105,239; whichapplication claims the benefit of U.S. Provisional Patent ApplicationNo. 60/765,887, filed Feb. 6, 2006; which applications are incorporatedby reference in their entirety herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is generally related to medical ultrasound, andmore particularly to a non-invasive screening method and apparatus forvisualizing coronary artery obstruction which solves artery curvatureproblems by projecting the three-dimensional volume onto atwo-dimensional screen, highlighting the arteries and veins in contrastto other tissue and the heart chambers by using nonlinear techniques,storing many processed 2D slices into a 3D volume, and projecting thevolume with varying view angles controlled by a pointing device.

2. Discussion of Related Art including information disclosed under 37CFR §§1.97, 1.98

In his well-respected textbook, Echocardiography, Harvey Feigenbaum,M.D., describes analysis techniques for many cardiac conditions. Most ofthese are used in routine clinical practice. An exception is thevisualization of the coronary arteries (CAs) and assessment of theircondition. Considering the seriousness of coronary artery disease, it isobvious that a non-invasive screening technique to assess obstruction inthese arteries would be of great importance.

At pages 482-490 of Echocardiography, 5^(th) Edition, Feigenbaum shows2D views of the coronary arteries. These studies prove that clinicalultrasound machines circa 1993 already had sufficient resolution toimage the larger parts of these arteries. However, as Feigenbaum states,the curved nature of the arteries usually does not permit them to beseen for any length in an individual frame. In addition, it takes greatskill and knowledge to recognize the arteries when they do come intoview. For these reasons, clinical ultrasound is rarely used to visualizethe CAs.

Because of the curved nature of the CAs, it would be advantageous anddesirable to visualize them in three dimensions. The current patentteaches the steps required to achieve this goal.

Several U.S. Patents and/or patent applications teach or show methods ofimaging coronary arteries using ultrasound or other medical imagingtechniques. Notable among them are U.S. Pat. Appln. No. 2006/0079782, byBeach et al., which shows an ultrasonic technique for visualizingcoronary arteries using a 2-D scan and a 3-D display.

U.S. Pat. Appln. No. 2006/0079759, by Vaillant et al., discloses amethod and apparatus for registering 3-D models of the heart usingultrasound.

U.S. Pat. Appln. No. 2005/0281447, by Moreau-Gobard et al., teaches amethod of producing a 3-D image of the coronary artery system usingultrasound.

U.S. Pat. Appln. No. 2005/0004449, by Mitschke et al., teaches the useof ultrasound to acquire preoperative 3-D images for marker-lessnavigation of a medical instrument.

U.S. Pat. Appln. No. 20020087071, by Schmitz et al., teaches a processfor graphic visualization and diagnosis of thrombi as well as the use ofparticle suspensions for the production of contrast media for thevisualization of thrombi (circumscribed blood solidification that formsin arteries or veins by intravascular clotting) through the use ofnuclear spin tomography. This method produces 3-D images from a 2-Dsource.

U.S. Pat. No. 6,148,095, to Prause et al., shows a method ofthree-dimensional reconstruction of coronary arteries by fusing databetween biplane angiography and IVUS frames of a pullback sequence. The3D course of the tortuous vessel is first determined from the angiogramsand then combined with the 2D representations regarding the 3D course(e.g., segmented IVUS frames of a pullback sequence) using a data fusionapparatus and method: The determination of the 3D pullback path isrepresented by the external energy of the tortuous vessel and theinternal energy of a line object such as a catheter.

U.S. Pat. Appln. No. 2005/0288588, by Weber et al., discloses a methodand apparatus for electronic volume data acquisition using ultrasoundgenerates image data in a coherent aperture combining beamforming(CAC-BF) scanning and imaging process.

U.S. Pat. No. 6,166,853, to Sapia et al., teaches use of an adaptivestructure of a Wiener filter to deconvolve three-dimensional wide-fieldmicroscope images for the purposes of improving spatial resolution andremoving out-of-focus light. The filter is a three-dimensional kernelrepresenting a finite-impulse-response (FIR) structure requiring on theorder of one thousand (1,000) taps or more to achieve an acceptablemean-square-error. Converging to a solution is done in thespatial-domain. Alternatively, a three-dimensional kernel representingan infinite-impulse-response (IIR) structure may be employed, as an IIRstructure typically requires fewer taps to achieve the same or betterperformance, resulting in higher resolution images with less noise andfaster computations.

The foregoing patents reflect the current state of the art of which thepresent inventor is aware. Reference to, and discussion of, thesepatents is intended to aid in discharging Applicant's acknowledged dutyof candor in disclosing information that may be relevant to theexamination of claims to the present invention. However, it isrespectfully submitted that none of the above-indicated patentsdisclose, teach, suggest, show, or otherwise render obvious, eithersingly or when considered in combination, the invention described andclaimed herein.

BRIEF SUMMARY OF THE INVENTION

The present invention is a method and apparatus for providingthree-dimensional images of the coronary arteries.

It is an object of the present invention to provide suchthree-dimensional images using ultrasound.

It is a further object of the present invention to provide anon-invasive screening test to assess the patency of the coronaryarteries.

It is yet another object of the present invention to provide a new andimproved means of evaluating the degree of obstruction in partiallyoccluded arteries.

It is still another object of the present invention to provide a new andimproved means to visualize coronary arteries which overcomes arterycurvature problems by projecting the three-dimensional volume onto atwo-dimensional screen.

It is still another object of the present invention is to provideimproved three dimensional images of coronary arteries by utilizinglinear filtering to reduce noise, using nonlinear techniques, such asneural networks, to highlight arteries and veins in contrast to othertissue and the heart chambers, storing numerous processed 2D slices intoa 3D volume, and then projecting the volume with varying view anglescontrolled by a pointing device.

An even further object of the present invention is to provide gyroscopicstabilization to the ultrasound probe to minimize unwanted angulationduring data collection.

The foregoing summary broadly sets out the more important features ofthe present invention so that the detailed description that follows maybe better understood, and so that the present contributions to the artmay be better appreciated. There are additional features of theinvention that will be described in the detailed description of thepreferred embodiments of the invention which will form the subjectmatter of the claims appended hereto.

Accordingly, before explaining the preferred embodiment of thedisclosure in detail, it is to be understood that the disclosure is notlimited in its application to the details of the construction and thearrangements set forth in the following description or illustrated inthe drawings. The inventive apparatus described herein is capable ofother embodiments and of being practiced and carried out in variousways.

Also, it is to be understood that the terminology and phraseologyemployed herein are for descriptive purposes only, and not limitation.Where specific dimensional and material specifications have beenincluded or omitted from the specification or the claims, or both, it isto be understood that the same are not to be incorporated into theappended claims.

As such, those skilled in the art will appreciate that the conception,upon which this disclosure is based may readily be used as a basis fordesigning other structures, methods, and systems for carrying out theseveral purposes of the present invention. It is important, therefore,that the claims are regarded as including such equivalent constructionsas far as they do not depart from the spirit and scope of the presentinvention. Rather, the fundamental aspects of the invention, along withthe various features and structures that characterize the invention, arepointed out with particularity in the claims annexed to and forming apart of this disclosure. For a better understanding of the presentinvention, its advantages and the specific objects attained by its uses,reference should be made to the accompanying drawings and descriptivematter in which there are illustrated the preferred embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood and objects other than those setforth above will become apparent when consideration is given to thefollowing detailed description thereof. Such description makes referenceto the annexed drawings wherein:

FIG. 1 is a schematic diagram illustrating probe angles discussed in theinstant disclosure;

FIG. 2 is a schematic block diagram showing a linear combiner;

FIG. 3 is a schematic block diagram showing the essential componentscomprising the inventive apparatus;

FIGS. 4 and 5 are screen shots showing simulated three-dimensionaldisplays of the coronary arteries.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is a method and apparatus that renders aprojection of images of the coronary arteries in three dimensions usingultrasound. In its most essential aspect, this is accomplished by firstproducing a 3D array of voxels indicating the blood-filled areas of theheart. Next, a 2D image of the blood-filled areas is projected as afunction of view angle and rotation, and this allows an observation andevaluation of the blood-filled areas from a number of view angles suchthat the coronary arteries and veins are seen unobscured by the majorchambers of the heart. The objective is to provide a non-invasivescreening test to assess the patency of the coronary arteries. It ishoped that in addition to detecting complete blockages of the arteries,it will also be possible to assess the degree of obstruction inpartially occluded arteries.

Several methods are available to obtain the necessary three dimensionalinformation using ultrasound. Two methods have been published concerningdirect 3D. One is the RT3D method developed by Dr. Von Ramm andassociates at Duke University (see, S. W. Smith, H. G. Pavy, and O. T.von Ramm, “High-speed ultrasound volumetric imaging-system. 1.Transducer design and beam steering,” IEEE Trans. Ultrason.,Ferroelect., Freq. Contr., vol. 38, pp. 100-108, 1991; and O. T. vonRamm, S. W. Smith, and H. G. Pavy, “High-speed ultrasound volumetricimaging-System. 2. Parallel processing and image display,” IEEE Trans.Ultrason., Ferroelect., Freq. Contr., vol. 38, pp. 109-115, 1991.)

Another direct 3D method is the CAC-BF method patent pending by Weber etal. (discussed supra).

Neither of the above-referenced direct 3D methods provides sufficientresolution to reliably image the coronary arteries. One reason for thisis that resolution is dependent on focusing of both the transmitted beamand the received energy. In order to capture a 3D image of the heartfast enough to freeze motion at a particular part of the cardiac cycle,the number of transmit pulses possible due to the speed of ultrasound intissue is very limited. These pulses cannot be sharply focused if one isto cover the entire volume. Although no such limitation applies to thefocusing of the received beam, the combination of transmit focusing andreceive focusing is not as sharp as is possible with a 2D scanner. Forthis reason, the preferred implementation of the present invention is toutilize the superior resolution of 2D scanners and store a sufficientnumber of closely spaced 2D slices to capture the structural features ofthe coronary arteries and other portions of the heart H.

Referring now to FIG. 1, the slices can be obtained by a sequence ofrotations of the probe 10 either in roll angle 20, or in pitch angle 30.In this context, roll angle means the degree of rotation about thecentral scan line of the 2D scanner, while pitch angle is the degree ofrotation about the line 40 of the probe transducers, or about the leastmean square fit line in the case of a curved array. Yaw angle isorthogonal to pitch and roll.

Several method steps are needed to capture the information and assembleit into a useful display. They are as follows:

First, an LMS adaptive filter or Wiener filter is used to removeaperture blur and speckle noise from each 2D image obtained. It is knownthat linear filtering can be very effective on 2D scans (see, inparticular, Sapia, M. A., Fox, M. D., Loew, L. M., Schaff, J. C.,“Ultrasound Image Deconvolution Using Adaptive Inverse Filtering”,12^(th) IEEE Symposium on Computer-Based Medical Systems, CBMS 1999, pp.248-253; Sapia, M. A., “Deconvolution of Ultrasonic Waveforms Using anAdaptive Wiener Filter,” Review of Progress in QuantitativeNondestructive Evaluation, Volume 13, Edited by D. O. Thompson and D. E.Chimenti, New York, Plenum Press, pp. 855-862, 1994; Mark Angelo Sapia,“Multi-Dimensional Deconvolution of Optical Microscope and UltrasoundImaging Using Adaptive Least-Mean-Square (LMS) Inverse Filtering,” Ph.D.Dissertation, University of Connecticut, 2000; Specht, D. F., Blinddeconvolution of motion blur using LMS inverse filtering, LockheedIndependent Research, unpublished, 1976); U.S. Pat. No. 6,166,853, toSapia et al.; U.S. Pat. Appl. No. 2005/0053305, by Li et al). This stepmay not be necessary, as “standard” 2D scanners are constantly improvingand may correct sufficiently for aperture blur.

Next, after collecting a sufficient number of closely spaced 2D slicesto represent the structures of the heart in the vicinity of the CAs,this information is used to fill the voxels of a 3D array. It is alsonecessary to compensate for non-uniform sampling and jitter in thepositioning of adjacent slices due to motion of the transducer and ofthe heart. Techniques to accomplish this are discussed in depth inNadkarni, Seemantini, “Cardiac Motion Synchronization for 3D CardiacUltrasound Imaging,” Ph.D. Dissertation, Univ. of Western Ontario, 2002,and Maria J. Ledesma-Carbayo et al., “Spatio-Temporal NonrigidRegistration for Ultrasound Cardiac Motion Estimation,” IEEE Trans. onMedical Imaging, v24, No. 9, September 2005, both of which areincorporated in their entirety by reference herein.

Next, in order to accomplish the previous step it is necessary to recordthe position and angle of each 2D slice relative to the other slices.This can be accomplished by a fixture positioned rigidly above thepatient's chest. However, the preferred embodiment of this inventionallows the sonographer to scan manually in order to find the best viewfor a particular patient. The sonographer must take care to scan overthe entire volume of interest. Redundant images are discarded oraveraged. A sensor to record relative instantaneous positions of theprobe may be attached to a standard handheld probe. A gyroscopicstabilizer attached to the probe is used to minimize angular variationsexcept on the axis desired. The gyroscope also provides a reference formeasuring the angular position of the probe. A sensor measures theposition and provides this information to the computer.

Then, in order to display the coronary arteries with maximum lumenopening, EKG gating or image-based synchronization is employed tocapture the images at only one part of the cardiac cycle. Thus the totalscan time will probably take many cardiac cycles. Nadkarni, cited above,shows that image-based synchronization is more reliable than thetraditional EKG synchronization, but it is more complicated. Thereforeimage-based synchronization is an alternate method that may be employed.

Next, the images are segmented. Linear filtering may not be effective inthe third dimension because the time between slices is necessarily largeand the beating heart cannot be considered a stationary target. Therewill also be jitter displacement between adjacent slices. It is not anobject of this invention simply to perform deconvolution of a pointspread function in three dimensions (although this could be done).Rather, as indicated above, it is an object of this invention todiscriminate between blood-filled areas and other areas of tissue so asto display only the blood-filled areas. These will include, in additionto the main chambers of the heart, the coronary arteries and veins. Itis a further object of this invention to separate the arteries and veinsfrom everything else so that they can be studied in detail. Theseparation is accomplished by further discriminating between largeblood-filled areas and narrow ones, such as arteries and veins, anddisplaying only the later. Several feedforward neural networks can beused for this step. All require training on examples of the twocategories (e.g. blood-filled areas and other tissue). The most-popularneural networks to be used in this step include: (1) the Multi-LayerPerceptron (discussed in Simon Haykin, Neural Networks: A ComprehensiveFoundation, 2^(nd) Edition, Prentice Hall, 1999); (2) the ProbabilisticNeural Network (variously considered in D. F. Specht, “ProbabilisticNeural Networks.” Neural Networks, vol. 3, pp 109-118, 1990; D. F.Specht, “Enhancements to Probabilistic Neural Networks,” Proc. IEEEInternational Joint Conference on Neural Networks, Baltimore, Md., June,1992; and D. F. Specht and H. Romsdahl, “Experience with Adaptive PNNand Adaptive GRNN,” Proc. IEEE International Joint Conference on NeuralNetworks, vol. 2, pp. 1203-1208, Orlando, Fla., June 1994; and (3) theSupport Vector Machine (discussed in Nello Cristianini and JohnShawe-Taylor, An Introduction to Support Vector Machines, CambridgeUniversity Press, 2000.

Alternatively, the broad categories of blood-filled areas and othertissue areas can be more specifically designated as tubular blood-filledareas vs. everything else, thereby letting the neural network suppressthe large blood-filled areas of the chambers of the heart. It isimportant that neural networks are not limited to linear relationships.In addition to classification of each voxel as either a tubularblood-filled or not, image segmentation can be further enhanced byinfluence of neighbor pixels or voxels, as described in M. Morrison andY. Attikiouzel, “A probabilistic neural network based image segmentationnetwork for magnetic resonance images,” in Proc. Conf. Neural Networks,Baltimore, Md., 1992, vol. 3, pp. 60-65. This paper describes an imagesegmentation process in which only neighboring pixels are considered,but neighboring voxels in the 3D representation can be used in thepresently inventive ultrasound application.

Alternatively, discrimination between blood-filled tissues and othertissues can be accomplished using techniques such as Classification andRegression Trees (CART), Hidden Markov Models (HMM) or Fuzzy Logic. Anefficient classifier is found in the latest version of the GeneralRegression Neural Network, described in a paper authored by the presentinventor, D. F. Specht, “GRNN with Double Clustering,” Proc. IEEEInternational Joint Conference on Neural Networks, Vancouver, Canada,Jul. 16-21, 2006.

Finally, the point of view of the 3D image is rotated so that one groupof coronary arteries at a time can be observed not obscured by theothers or non-suppressed portions of the main chambers of the heart orother artifacts.

Linear filtering such as LMS or Wiener filtering is very effective whenthe point spread function of the imaging system is known and the objectis stationary. The point spread function can be measured using a knowntissue phantom when the object is reasonably stationary during a single2D scan with duration on the order of 1/30 second. However, the secondcondition does not apply for the time required to acquire a 3D volume.For this reason a linear deconvolution filter is not a good choice forthe third dimension. An artificial neural network (“ANN”) has thecapacity to select the surrounding voxels that contribute to a reliableclassification while rejecting the voxels that detract, and thenweighting those that are intermediate. Clearly the voxels within a given2D slice will have more relevance than the others, but significantinformation can be extracted from adjacent slices.

A second reason for using a nonlinear filter is that small echos fromsolid tissue and large echos from solid tissue, including specularreflections and speckle patterns, all must be displayed similarly assmall values compared to those of the blood-filled areas. Althoughlinear filtering with thresholding could accomplish this portion of thetask, ANNs are inherently nonlinear.

The Wiener Filter: The Wiener filter is not new, but since it isimportant for the debluring step, it will be described briefly here inthe context of the present invention.

The Wiener filter is the mean squared error optimal stationary linearfilter for images degraded by additive noise and blurring. Wienerfilters are usually applied in the frequency domain. Given a degradedimage i(n,m), one takes the Discrete Fourier Transform (DFT) or the FastFourier Transform (FFT) to obtain I(u,v). The true image spectrum isestimated by taking the product of I(u,v) with the Wiener filter G(u,v):Ŝ=G(u,v)I(u,v)

The inverse DFT or FFT is then used to obtain the image estimate s(n,m)from its spectrum. The Wiener filter is defined in terms of thesespectra:

H(u,v) Fourier transform of the point spread function (psf)

P_(s)(u,v) Power spectrum of the signal process, obtained by taking theFourier transform of the signal autocorrelation

P_(n)(u,v) Power spectrum of the noise process, obtained by taking theFourier transform of the noise autocorrelation

The Wiener filter is:

${G( {u,v} )} = \frac{H*( {u,v} ){P_{s}( {u,v} )}}{{{{H( {u,v} )}}^{2}{P_{s}( {u,v} )}} + {P_{n}( {u,v} )}}$

The ratio P_(s)/P_(n) can be interpreted as signal-to-noise ratio. Atfrequencies with high signal to noise ratio, the Wiener filter becomesH⁻¹(u,v), the inverse filter for the psf. At frequencies for which thesignal to noise ratio is low, the Wiener filter tends to 0 and blocksthem out.

P_(s)(u,v)+P_(n)(u,v)=|I(u, v)|². The right hand function is easy tocompute from the Fourier transform of the observed data. P_(n)(u,v) isoften assumed to be constant over (u,v). It is then subtracted from thetotal to yield P_(s)(u,v).

The psf can be measured by observing a wire phantom in a tank using theultrasound instrument. The Fourier transform of the psf can then bestored for later use in the Wiener filter when examining patients.

Because the psf is not constant as a function of range, the Wienerfilter will have to be applied separately for several range zones andthe resulting images will have to be pieced together to form one imagefor display. A useful compromise might be to optimize the Wiener filterjust for the range of the object of interest such as a coronary arteryor valve.

An Adaptive Inverse Filter: As pointed out by Sapia and Fox (1999), anadaptive filter in the spatial domain is essentially equivalent to theWiener Filter implemented in the frequency domain and has someadditional advantages. The main advantages are the simplicity of thealgorithm, and that, being adaptive, it minimizes sources of noise suchas edge effects in addition to deconvolution of blurring resulting fromthe point spread function of the system.

In the spatial domain a transversal filter 100 as in FIG. 2 is used toproduce an improved signal y 110 as a weighted average of a finitenumber of inputs x₀ through x_(n−1) 120. In the case of ultrasound, thegreatest blurring is transverse to the direction of the beam and so themost significant improvement will be in this direction. For this reason,all of the inputs, x, may be taken centered around the pixel y to beestimated and from the same distance from the transducer (e.g. from thesame line in the transverse direction). The same algorithm applieswithout change if some of the x's are taken from adjacent transverselines. The method to solve for the optimum weight vector is to use anadaptive least-mean-square (LMS) algorithm. The LMS solution convergesnumerically to the optimum solution.

Let X_(k) be a N-dimensional vector of the inputs used for estimatingy[k], and let W_(k) be the set of weights after training k samples. Thelinear estimate when trained will be y[k]=X_(k) ^(T) W.

For training it is necessary to know the desired value of the outputpixels, d[k] 130. These can be obtained by imaging a phantom with knowngeometry. After each training iteration, the error can be evaluated asε_(k) =d[k]−X _(k) ^(T) W _(k).

The LMS algorithm in equation form is:W _(k+1) =W _(k)+2με_(k) X _(k)

where μ is the convergence coefficient. [See B. Widrow and S. D.Stearns, Adaptive Signal Processing, Englewood Cliffs, N.J.,Prentice-Hall, pp. 99-137, 1985.]

Because the psf is not constant as a function of range, the adaptiveinverse filter also will have to be applied separately for several rangezones and the resulting images will have to be pieced together to formone image for display.

Required Hardware: A typical hardware system 200 incorporating theinvention is shown in FIG. 3. The central component is a standardcardiac ultrasound scanner 210 such as those marketed by Siemens,General Electric, Philips, or Toshiba.

The standard scanner includes a phased array probe 220 shown separately.Mechanically attached to the probe is a gyroscopic stabilizer andposition sensor 230 which must, at minimum, measure the relative angleof the probe as it is rotated to insonify the volume of the heart. Forthe purpose intended, it need not insonify the entire heart muscle, butonly the main coronary arteries. The angle could be referenced to astationary fixture. However, because only the angle is required, thereference can be a gyroscope attached to the probe. Small variations inthe location of the probe from one scan to the next can be compensatedby software using correlation or related techniques. A more-complexmodel could use integrating accelerometers to maintain exact positioninformation.

The output of the standard scanner is displayed on a monitor 240. Thedisplay typically has several modes including 2D sector, M mode, anddoppler.

A computer 250 is necessary to implement the software algorithmsdescribed. Inputs to the computer include the processed scan linereturns 260 from the sector scanner, the position sensors 270 mentionedabove, some sort of pointing device such as a mouse 280, and usuallyelectrocardiogram sensor input 290 from an EKG sensor 300 forsynchronization. The scan line information needed for LMS filtering orWiener filtering is that of individual scan lines after beam formationbut before scan conversion for display. The scan line information neededfor discrimination, classification, and segmentation is the output 310of the scan converter 320 because dimensions are constant after thisconversion. After discrimination, classification, and segmentation, a 3Dimage is displayed on a monitor 330.

FIG. 3 shows the components necessary for an add-on (after market)system. In this figure, the scan converter is shown separate from therest of the unit to indicate that the add-on computer optionally wouldtap into the scan line information before scan conversion and thenreform deconvoluted scan line information to the scan conversion.However, much integration is possible. In practice, the two separatemonitors could actually be the same monitor with 3D display as aseparate mode. Standard ultrasound sector scanners typically include EKGamplifiers, so there is no need to duplicate these. Standard ultrasoundsector scanners always include a computer. That computer and thecomputer component shown could be combined into one computer with bothsets of software integrated.

Simulated Display: Using the inventive method, a simulatedrepresentation of the coronary arteries and veins has been formed in athree dimensional format. Two-dimensional projections have been formedunder control of a mouse to select view angles to best highlight theareas of interest. Results from two particular view angles 400, and 500,respectively, are reproduced in FIGS. 4 and 5. In FIG. 4 the volume wasrotated to show the left coronary artery, the bifurcation to anteriordescending and to circumflex branch. Parallel to both branches is thegreat cardiac vein. In FIG. 5 the volume was rotated to show the rightcoronary artery.

Gyroscopic Stabilization and Angle Measurement: When collecting data fora 3D presentation, it is important to keep the phased array transducersin the same position while the probe is being rotated. Otherwise thespatial position (x, y, and z) and the angular position (pitch, yaw, androll) must be continuously measured and compensated for by software. Thefixture mounted over the patient mentioned in the previous section canassure that, of the six degrees of freedom, all are fixed during thecollection of data except either roll or pitch, which is intentionallychanged and measured. Some compensation for spatial position will stillhave to be done (by correlation techniques) because of the motion of theheart. The fixture satisfies the technical requirements for theinstrumentation, but sacrifices the convenience that comes with handpositioning of the probe to find the best view for a particular patient.

A preferred embodiment of the probe for this invention includes thefollowing: First and second gyroscopes mechanically attached to astandard phased array probe designed for two dimensional scans. Thefirst gyroscope is aligned with the center line of the sector scan(i.e., perpendicular to the surface of the skin and aimed at the tissueto be imaged). The second gyroscope is mounted perpendicular to thealignment of the first gyroscope with a sensor to measure the relativeangle between it and the probe handle as it is mechanically rotated.

The first gyroscope is motorized and has sufficient mass and rotationalspeed to stabilize the handheld probe in pitch and yaw even though thetechnician's hand might apply slight pressure to twist it in pitch oryaw. The second gyroscope can be much smaller in mass (or it could besolid state, such as piezoelectric) as its only function is to measurethe rotation angle of the probe.

In operation, the gyroscopes would initially be turned off while thetechnician positions the probe to find the cone which best contains thevolume of interest. When he or she is satisfied with the orientation ofthe probe, he or she turns on the gyroscopes to stabilize thatorientation and then scans through the range of rotation angles (180degrees covers the entire cone, but a more limited range may besufficient). The scanning through rotation angles may be done by hand orit could be automated.

An alternate embodiment of the probe for this invention includes thefollowing: First and second gyroscopes mechanically attached to astandard phased array probe designed for two dimensional scans. Thefirst gyroscope is aligned parallel to the line of transducer elementsin the probe (or the closest mean-squared-error fit line in the case ofa curved array). It is not instrumented for angle measurement. Thesecond gyroscope is mounted perpendicular to the alignment of the firstgyroscope with a sensor to measure the relative angle between it and theprobe handle.

The first gyroscope is motorized and has sufficient mass and rotationalspeed to stabilize the handheld probe in yaw and roll, even though thetechnician's hand might apply slight pressure to twist it in yaw androll. For these purposes, pitch angle is defined as angle of rotationabout the axis of the first gyroscope. The second gyroscope can be muchsmaller in mass (or it could be solid state, such as piezoelectric) asits only function is to measure the pitch angle.

In operation, the gyroscopes would initially be turned off while thetechnician positions the probe to find the best range of views. When heor she is satisfied with the orientation of the probe, he or she turnson the gyroscopes to stabilize that orientation and then scans throughthe range of pitch angles.

Another implementation of the probe is to have a two dimensional arrayof phased array transducers so that the angles can be adjustedelectronically.

The above disclosure is sufficient to enable one of ordinary skill inthe art to practice the invention, and provides the best mode ofpracticing the invention presently contemplated by the inventor. Whilethere is provided herein a full and complete disclosure of the preferredembodiments of this invention, it is not desired to limit the inventionto the exact construction, dimensional relationships, and operationshown and described. Various modifications, alternative constructions,changes and equivalents will readily occur to those skilled in the artand may be employed, as suitable, without departing from the true spiritand scope of the invention. Such changes might involve alternativematerials, components, structural arrangements, sizes, shapes, forms,functions, operational features or the like.

Therefore, the above description and illustrations should not beconstrued as limiting the scope of the invention, which is defined bythe appended claims.

What is claimed is:
 1. A method of ultrasound imaging, comprising:placing a handheld 2D ultrasound scanner in contact with skin of apatient; obtaining a plurality of 2D ultrasound scans of a patient'sheart with the handheld 2D ultrasound scanner; filling a plurality ofvoxels of a 3D array with the plurality of 2D ultrasound scans; applyingan image processing technique to classify the voxels as representingeither a tubular blood-filled tissue structure or a non-tubularblood-filled tissue structure; and rejecting the voxels representing thenon-tubular blood-filled structure.
 2. The method of claim 1 wherein theimage processing technique comprises a classification and regressiontree.
 3. The method of claim 1 wherein the image processing techniquecomprises a hidden Markov model.
 4. The method of claim 1 wherein theimage processing technique comprises fuzzy logic.
 5. The method of claim1 wherein the image processing technique comprises an artificial neuralnetwork.
 6. The method of claim 1 wherein the recording step comprisesrecording a plurality of 2D ultrasound scans of heart tissue andcoronary arteries of the patient's heart.
 7. The method of claim 1wherein the obtaining step further comprises obtaining the plurality of2D ultrasound scans by constraining rotation of the handheld 2Dultrasound scanner in a roll angle.
 8. The method of claim 1 wherein theobtaining step further comprises obtaining the plurality of 2Dultrasound scans by constraining rotation of the handheld 2D ultrasoundscanner in a pitch angle.
 9. The method of claim 7 wherein theconstraining is achieved by stabilizing the handheld 2D ultrasoundscanner with a gyroscopic stabilizer.
 10. The method of claim 8 whereinthe constraining is achieved by stabilizing the handheld 2D ultrasoundscanner with a gyroscopic stabilizer.